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    <span style="font-size:42px">GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing</span>
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                <span style="font-size:20px"><a href="https://xiaohongliu.ca">Xiaohong Liu</a><sup>*</sup></span>
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                <span style="font-size:20px">Yongrui Ma<sup>*</sup></span>
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                <span style="font-size:20px">Zhihao Shi</span>
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                <span style="font-size:20px"><a href="http://www.ece.mcmaster.ca/~junchen/">Jun Chen</a></span>
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                <span style="font-size:20px">McMaster University</span>
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                <span style="font-size:20px;color:red"><a href="http://iccv2019.thecvf.com/">ICCV, 2019</a></span>
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<p align="justify" style="font-size: 18px">
    We propose an end-to-end trainable Convolutional Neural Network (CNN), named GridDehazeNet, for single image
    dehazing. The GridDehazeNet consists of three modules: pre-processing, backbone, and post-processing. The trainable
    pre-processing module can generate learned inputs with better diversity and more pertinent features as compared to
    those derived inputs produced by hand-selected pre-processing methods. The backbone module implements a novel
    attention-based multi-scale estimation on a grid network, which can effectively alleviate the bottleneck issue often
    encountered in the conventional multi-scale approach. The post-processing module helps to reduce the artifacts in
    the final output. Experimental results indicate that the GridDehazeNet outperforms the state-of-the-arts on both
    synthetic and real-world images. The proposed hazing method does not rely on the atmosphere scattering model, and we
    provide an explanation as to why it is not necessarily beneficial to take advantage of the dimension reduction
    offered by the atmosphere scattering model for image dehazing, even if only the dehazing results on synthetic images
    are concerned.
    * authors contributed equally
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<span>* Authors contributed equally</span>
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<center><h1>News</h1></center>
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    <li>[August 2019] Paper is released on <a href="https://arxiv.org/abs/1908.03245">arXiv</a></li>
    <li>[August 2019] Code released on <a href="https://github.com/proteus1991/GridDehazeNet">Github</a></li>
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<center><h1>Paper</h1></center>
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        <td><a href="./resource/GridDehazeNet.pdf"><img style="height:180px; border: solid; border-radius:30px;"
                            src="./resource/paper_cover.png"></a></td>
        <td><span style="font-size:18px">Xiaohong Liu* , Yongrui Ma* , Zhihao Shi , Jun Chen<br>
                          <small>(* equal contribution)</small><br><br>
                          GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing<br><br>

			  <a href="http://iccv2019.thecvf.com/">ICCV, 2019</a> (to appear).<br>
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                    <span style="font-size:18px"><center>
                      <a href="./resource/GridDehazeNet.pdf">[PDF]</a>
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        <td><span style="font-size:18px"><center>
                      <a href="./resource/bibtex.txt">[Bibtex]</a>
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        <td><span style="font-size:18px"><center>
                      <a href="https://github.com/proteus1991/GridDehazeNet">[Code]</a>
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<center><h1>The architecture of GridDehazeNet</h1></center>
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    <img class="round" style="height:500" src="./resource/GridDehazeNet_main.png"></a>
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<center><h1>Illustration of the dash block</h1></center>
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    <img class="round" style="height:500" src="./resource/GridDehazeNet_block.png"></a>
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<center><h1> Qualitative Comparisons</h1></center>
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                <a href="./resource/SOTS.png"><img src="./resource/SOTS.png" width="900px"></a><br>
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                            Qualitative comparisons on SOTS.
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                <a href="./resource/Real_world.png"><img src="./resource/Real_world.png" width="900px"></a><br>
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                    <span style="font-size:14px">
                        Qualitative comparisons on the real-world dataset.
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<center><h1> Quantitative Comparisons</h1></center>
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                <a href="./resource/Comparisons.png"><img src="./resource/Comparisons.png" width="900px"></a><br>
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                        Comparisons on SOTS and Sun RGB-D for different methods
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                <a href="./resource/Different_variants.png"><img src="./resource/Different_variants.png" width="900px"></a><br>
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                    <span style="font-size:14px">
                        Comparisons on SOTS for different variants of GridDehazeNet.
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                <a href="./resource/Computational_Complexity.png"><img src="./resource/Computational_Complexity.png"
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                    <span style="font-size:14px">
                        Runtime comparison of different dehazing methods.
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